{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "2e8f33f4",
      "metadata": {},
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pinecone-io/examples/blob/master/learn/analytics-and-ml/model-training/training-with-wandb/01-encoder-train.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](https://nbviewer.org/github/pinecone-io/examples/blob/master/learn/analytics-and-ml/model-training/training-with-wandb/01-encoder-train.ipynb)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "87ddd7ce-1e2f-4bd9-a35e-7f3d9942acc3",
      "metadata": {
        "id": "87ddd7ce-1e2f-4bd9-a35e-7f3d9942acc3",
        "tags": []
      },
      "outputs": [],
      "source": [
        "!pip install -qq wandb datasets pinecone-client sentence-transformers transformers"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "6180eaec-91bd-434d-98bd-da2239eccad5",
      "metadata": {
        "id": "6180eaec-91bd-434d-98bd-da2239eccad5"
      },
      "source": [
        "## Encoder Training\n",
        "\n",
        "This is part *two* of a four-part notebook series on fine-tuning encoder models with Weights & Biases for use with Pinecone. Find the [full set of notebooks on Github here](https://github.com/pinecone-io/examples/blob/master/analytics-and-ml/model-training/training-with-wandb).\n",
        "\n",
        "We begin by initializing our connection to W&B, if you need an API key they can be found (for free) at [WandB.ai](https://wandb.ai). If you haven't already created the project, the following will create a new project called `arxiv-searching`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d09f42e7-a811-4f0e-8596-72b58f5be4ac",
      "metadata": {
        "id": "d09f42e7-a811-4f0e-8596-72b58f5be4ac",
        "outputId": "db26f9ba-756a-4b1f-8b2b-8be4508d640a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/opt/conda/lib/python3.7/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
            "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mjamesbriggs\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "Tracking run with wandb version 0.13.5"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Run data is saved locally in <code>/home/jupyter/wandb/wandb/run-20221109_075920-22328p01</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Syncing run <strong><a href=\"https://wandb.ai/jamesbriggs/arxiv-searching/runs/22328p01\" target=\"_blank\">still-moon-31</a></strong> to <a href=\"https://wandb.ai/jamesbriggs/arxiv-searching\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import wandb\n",
        "run = wandb.init(project=\"arxiv-searching\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aeba921e-0e7e-460a-9da4-daed871c28fe",
      "metadata": {
        "id": "aeba921e-0e7e-460a-9da4-daed871c28fe"
      },
      "source": [
        "We begin by downloading what will be our train and validation datasets generated by the **T5** model in this [previous notebook from W&B](https://colab.research.google.com/drive/1IemM772X54ND--WtEx61ENMTzeVv17eu?usp=sharing). Both of these exist as artifacts in W&B:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1981d4f3-b91a-4482-ab5b-e5b60bf81b71",
      "metadata": {
        "id": "1981d4f3-b91a-4482-ab5b-e5b60bf81b71",
        "outputId": "57a05515-069b-4d68-cba1-3d56a06adbc0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact arxiv-papers:latest, 3388.22MB. 1 files... \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
            "Done. 0:0:0.1\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
          ]
        }
      ],
      "source": [
        "train_artifact = run.use_artifact(\n",
        "    'events/arxiv-searching/arxiv-papers:latest', type='dataset'\n",
        ")\n",
        "train_artifact_dir = train_artifact.download()\n",
        "val_artifact = run.use_artifact(\n",
        "    'events/arxiv-searching/arxiv-predictions:latest', type='test_dataset_w_predictions'\n",
        ")\n",
        "val_artifact_dir = val_artifact.download()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "uv6nr7dnSyST",
      "metadata": {
        "id": "uv6nr7dnSyST"
      },
      "source": [
        "These are downloaded to two directories:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dd9c0e71-78c4-486a-b546-5eb9852efb0c",
      "metadata": {
        "id": "dd9c0e71-78c4-486a-b546-5eb9852efb0c",
        "outputId": "0a2da4a8-6a20-4b56-ef82-202cb697eda8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'./artifacts/arxiv-papers:v0'"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_artifact_dir"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9ea108c9-629c-4016-8d2a-aebfdf3e877b",
      "metadata": {
        "id": "9ea108c9-629c-4016-8d2a-aebfdf3e877b",
        "outputId": "8b720042-2b23-437f-fa44-4d5cd6238eb3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(['arxiv-snapshot'], ['.ipynb_checkpoints', 'arxiv-test-dataset'])"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "os.listdir(train_artifact_dir), os.listdir(val_artifact_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5izXMc0iS3h1",
      "metadata": {
        "id": "5izXMc0iS3h1"
      },
      "source": [
        "The train file is a *huge* JSON lines with ~2M+ rows, whereas the validation file is a small ~1000 line CSV. We can load them like so:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9b43d856-6bf9-419b-b07f-6fefe11c211f",
      "metadata": {
        "id": "9b43d856-6bf9-419b-b07f-6fefe11c211f"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import json\n",
        "\n",
        "train_path = train_artifact_dir+\"/\"+os.listdir(train_artifact_dir)[-1]\n",
        "val_path = val_artifact_dir+\"/\"+os.listdir(val_artifact_dir)[-1]\n",
        "\n",
        "def load_json_lines(path):\n",
        "    with open(path, 'r') as f:\n",
        "        for line in f:\n",
        "            line_dict = json.loads(line)\n",
        "            yield line_dict\n",
        "\n",
        "train_metadata = load_json_lines(train_path)\n",
        "val_metadata = pd.read_csv(val_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "TMcMKfdmTJ4O",
      "metadata": {
        "id": "TMcMKfdmTJ4O"
      },
      "source": [
        "We keep the training set as a generator function rather than loading it all into memory at once."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d3e3bf3f-9060-4ff2-94ff-b2a206de59d7",
      "metadata": {
        "id": "d3e3bf3f-9060-4ff2-94ff-b2a206de59d7",
        "outputId": "a999d660-840d-4558-d9a2-4aeba0016f0c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<generator object load_json_lines at 0x7f82d0cb35d0>"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_metadata"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1c5c932a-3b31-4e6b-a723-aaf88fc3fd4d",
      "metadata": {
        "id": "1c5c932a-3b31-4e6b-a723-aaf88fc3fd4d",
        "outputId": "c5cf0005-d135-4baf-fc93-28d27a352acd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'id': '0704.0001', 'submitter': 'Pavel Nadolsky', 'authors': \"C. Bal\\\\'azs, E. L. Berger, P. M. Nadolsky, C.-P. Yuan\", 'title': 'Calculation of prompt diphoton production cross sections at Tevatron and\\n  LHC energies', 'comments': '37 pages, 15 figures; published version', 'journal-ref': 'Phys.Rev.D76:013009,2007', 'doi': '10.1103/PhysRevD.76.013009', 'report-no': 'ANL-HEP-PR-07-12', 'categories': 'hep-ph', 'license': None, 'abstract': '  A fully differential calculation in perturbative quantum chromodynamics is\\npresented for the production of massive photon pairs at hadron colliders. All\\nnext-to-leading order perturbative contributions from quark-antiquark,\\ngluon-(anti)quark, and gluon-gluon subprocesses are included, as well as\\nall-orders resummation of initial-state gluon radiation valid at\\nnext-to-next-to-leading logarithmic accuracy. The region of phase space is\\nspecified in which the calculation is most reliable. Good agreement is\\ndemonstrated with data from the Fermilab Tevatron, and predictions are made for\\nmore detailed tests with CDF and DO data. Predictions are shown for\\ndistributions of diphoton pairs produced at the energy of the Large Hadron\\nCollider (LHC). Distributions of the diphoton pairs from the decay of a Higgs\\nboson are contrasted with those produced from QCD processes at the LHC, showing\\nthat enhanced sensitivity to the signal can be obtained with judicious\\nselection of events.\\n', 'versions': [{'version': 'v1', 'created': 'Mon, 2 Apr 2007 19:18:42 GMT'}, {'version': 'v2', 'created': 'Tue, 24 Jul 2007 20:10:27 GMT'}], 'update_date': '2008-11-26', 'authors_parsed': [['Balázs', 'C.', ''], ['Berger', 'E. L.', ''], ['Nadolsky', 'P. M.', ''], ['Yuan', 'C. -P.', '']]}\n"
          ]
        }
      ],
      "source": [
        "# Show us a single item\n",
        "for doc in train_metadata:\n",
        "    print(doc)\n",
        "    break"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bID20HzRTVVh",
      "metadata": {
        "id": "bID20HzRTVVh"
      },
      "source": [
        "Our validation set is tiny so we load it all in-memory as a Pandas dataframe:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "43d422bd-710c-409e-bdea-952bc98a4707",
      "metadata": {
        "id": "43d422bd-710c-409e-bdea-952bc98a4707",
        "outputId": "2980bf5d-38e0-48c0-b240-6f3aa29167a8"
      },
      "outputs": [
        {
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              "      <th>Unnamed: 0</th>\n",
              "      <th>doi</th>\n",
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              "      <td>10.1145/3472538.3472587</td>\n",
              "      <td>2021</td>\n",
              "      <td>['cs.LG', 'cs.AI']</td>\n",
              "      <td>Adversarial Random Forest Classifier for Autom...</td>\n",
              "      <td>Autonomous game design, generating games algor...</td>\n",
              "      <td>Random Forest Classifier for Automated Game De...</td>\n",
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              "      <td>['cs.LO', 'cs.AI']</td>\n",
              "      <td>Proceedings 37th International Conference on L...</td>\n",
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              "      <td>Proceedings 37th International Conference on L...</td>\n",
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              "      <td>10.1103/PhysRevLett.127.237401</td>\n",
              "      <td>2021</td>\n",
              "      <td>['cond-mat.other', 'cond-mat.mtrl-sci']</td>\n",
              "      <td>Natural Negative-Refractive-Index Materials</td>\n",
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              "      <td>We prove quartic convergence of cubic spline i...</td>\n",
              "      <td>Convergence of cubic Riemannian splines</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4101</td>\n",
              "      <td>10.1103/PhysRevD.104.L121301</td>\n",
              "      <td>2021</td>\n",
              "      <td>['astro-ph.CO']</td>\n",
              "      <td>Model-independent versus model-dependent inter...</td>\n",
              "      <td>The traditional clustering analyses of galaxy ...</td>\n",
              "      <td>Bridging the Gap Between Model-Independent and...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>4130</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2021</td>\n",
              "      <td>['cs.LG', 'stat.ML']</td>\n",
              "      <td>Multitask Learning for Citation Purpose Classi...</td>\n",
              "      <td>We present our entry into the 2021 3C Shared T...</td>\n",
              "      <td>Multitask Learning for Citation Purpose Classi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1109</th>\n",
              "      <td>1398</td>\n",
              "      <td>10.1007/978-3-030-93413-2_29</td>\n",
              "      <td>2021</td>\n",
              "      <td>['cs.SI', 'cs.LG']</td>\n",
              "      <td>Towards control of opinion diversity by introd...</td>\n",
              "      <td>We explore a method to influence or even contr...</td>\n",
              "      <td>Control of opinion diversity by introducing ze...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1110</th>\n",
              "      <td>4919</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2021</td>\n",
              "      <td>['cs.CR']</td>\n",
              "      <td>Privacy-Preserving Mutual Authentication and K...</td>\n",
              "      <td>The usage of different technologies and smart ...</td>\n",
              "      <td>Privacy-Preserving Key Agreement Scheme for Mu...</td>\n",
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              "      <th>1111</th>\n",
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              "      <td>10.1137/20M1354982</td>\n",
              "      <td>2021</td>\n",
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              "      <td>Simplex-Structured Matrix Factorization: Spars...</td>\n",
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              "      <td>2021</td>\n",
              "      <td>['math.OC']</td>\n",
              "      <td>Approximately controllable finite-dimensional ...</td>\n",
              "      <td>We show that a bilinear control system is appr...</td>\n",
              "      <td>Controllable finite-dimensional bilinear systems</td>\n",
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              "      Unnamed: 0                             doi  year  \\\n",
              "0           4384         10.1145/3472538.3472587  2021   \n",
              "1           4741               10.4204/EPTCS.345  2021   \n",
              "2           5127  10.1103/PhysRevLett.127.237401  2021   \n",
              "3           2463                             NaN  2021   \n",
              "4           4101    10.1103/PhysRevD.104.L121301  2021   \n",
              "...          ...                             ...   ...   \n",
              "1108        4130                             NaN  2021   \n",
              "1109        1398    10.1007/978-3-030-93413-2_29  2021   \n",
              "1110        4919                             NaN  2021   \n",
              "1111        1606              10.1137/20M1354982  2021   \n",
              "1112        3431  10.1016/j.sysconle.2021.105028  2021   \n",
              "\n",
              "                                         cat_all  \\\n",
              "0                             ['cs.LG', 'cs.AI']   \n",
              "1                             ['cs.LO', 'cs.AI']   \n",
              "2        ['cond-mat.other', 'cond-mat.mtrl-sci']   \n",
              "3                           ['math.NA', 'cs.NA']   \n",
              "4                                ['astro-ph.CO']   \n",
              "...                                          ...   \n",
              "1108                        ['cs.LG', 'stat.ML']   \n",
              "1109                          ['cs.SI', 'cs.LG']   \n",
              "1110                                   ['cs.CR']   \n",
              "1111  ['cs.LG', 'eess.IV', 'eess.SP', 'stat.ML']   \n",
              "1112                                 ['math.OC']   \n",
              "\n",
              "                                            target_text  \\\n",
              "0     Adversarial Random Forest Classifier for Autom...   \n",
              "1     Proceedings 37th International Conference on L...   \n",
              "2           Natural Negative-Refractive-Index Materials   \n",
              "3     Quartic $L^p$-convergence of cubic Riemannian ...   \n",
              "4     Model-independent versus model-dependent inter...   \n",
              "...                                                 ...   \n",
              "1108  Multitask Learning for Citation Purpose Classi...   \n",
              "1109  Towards control of opinion diversity by introd...   \n",
              "1110  Privacy-Preserving Mutual Authentication and K...   \n",
              "1111  Simplex-Structured Matrix Factorization: Spars...   \n",
              "1112  Approximately controllable finite-dimensional ...   \n",
              "\n",
              "                                             input_text  \\\n",
              "0     Autonomous game design, generating games algor...   \n",
              "1     ICLP is the premier international event for pr...   \n",
              "2     Our calculation shows that negative refractive...   \n",
              "3     We prove quartic convergence of cubic spline i...   \n",
              "4     The traditional clustering analyses of galaxy ...   \n",
              "...                                                 ...   \n",
              "1108  We present our entry into the 2021 3C Shared T...   \n",
              "1109  We explore a method to influence or even contr...   \n",
              "1110  The usage of different technologies and smart ...   \n",
              "1111  In this paper, we provide novel algorithms wit...   \n",
              "1112  We show that a bilinear control system is appr...   \n",
              "\n",
              "                                   summarized_new_title  \n",
              "0     Random Forest Classifier for Automated Game De...  \n",
              "1     Proceedings 37th International Conference on L...  \n",
              "2           Natural Negative-Refractive-Index Materials  \n",
              "3               Convergence of cubic Riemannian splines  \n",
              "4     Bridging the Gap Between Model-Independent and...  \n",
              "...                                                 ...  \n",
              "1108  Multitask Learning for Citation Purpose Classi...  \n",
              "1109  Control of opinion diversity by introducing ze...  \n",
              "1110  Privacy-Preserving Key Agreement Scheme for Mu...  \n",
              "1111  Simplex-Structured Matrix Factorization: Spars...  \n",
              "1112   Controllable finite-dimensional bilinear systems  \n",
              "\n",
              "[1113 rows x 7 columns]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "val_metadata.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "VvWgMqoCT7Y5",
      "metadata": {
        "id": "VvWgMqoCT7Y5"
      },
      "source": [
        "The equivalent features that we care about here are:\n",
        "\n",
        "| train | val |\n",
        "| ----- | --- |\n",
        "| `abstract` | `input_text` |\n",
        "| `title` | `summarized_new_title` |"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b865b248-7bc5-4594-8f4e-cb927bfcc368",
      "metadata": {
        "id": "b865b248-7bc5-4594-8f4e-cb927bfcc368",
        "outputId": "f5ab60d0-a3a1-4df8-bfa6-dd68c2c590d0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Unnamed: 0                                                           4384\n",
              "doi                                               10.1145/3472538.3472587\n",
              "year                                                                 2021\n",
              "cat_all                                                ['cs.LG', 'cs.AI']\n",
              "target_text             Adversarial Random Forest Classifier for Autom...\n",
              "input_text              Autonomous game design, generating games algor...\n",
              "summarized_new_title    Random Forest Classifier for Automated Game De...\n",
              "Name: 0, dtype: object"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "val_metadata.iloc[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4d8dc116-ca32-4bd7-af46-2df0457b9221",
      "metadata": {
        "id": "4d8dc116-ca32-4bd7-af46-2df0457b9221"
      },
      "source": [
        "We want the `title` and `abstract` (or equivalent) to act as our *anchor*, *positive* pair respectively. To do this we will create a dataset object that will handle loading these values in the required format."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a66c1210-857f-4166-9426-7708ec8caa32",
      "metadata": {
        "id": "a66c1210-857f-4166-9426-7708ec8caa32",
        "outputId": "c0b844aa-be3d-4eca-bcb1-bb19be072e5c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/opt/conda/lib/python3.7/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
            "  from .autonotebook import tqdm as notebook_tqdm\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "import torch\n",
        "\n",
        "model_id = 'microsoft/MiniLM-L12-H384-uncased'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "\n",
        "class ArxivDataset(torch.utils.data.IterableDataset):\n",
        "    def __init__(self, data_gen):\n",
        "        super(ArxivDataset).__init__()\n",
        "        # this depends on your dataset, suppose your dataset contains \n",
        "        # images whose path you save in this list\n",
        "        self.dataset = data_gen\n",
        "\n",
        "    def __iter__(self):\n",
        "        for doc in self.dataset:\n",
        "            anchor = tokenizer(\n",
        "                doc['title'], max_length=512,\n",
        "                padding='max_length', truncation=True,\n",
        "                return_tensors='pt'\n",
        "            )\n",
        "            positive = tokenizer(\n",
        "                doc['abstract'], max_length=512,\n",
        "                padding='max_length', truncation=True,\n",
        "                return_tensors='pt'\n",
        "            )\n",
        "            sample = {\n",
        "                'anchor': {\n",
        "                    'input_ids': anchor['input_ids'],\n",
        "                    'attention_mask': anchor['attention_mask']\n",
        "                },\n",
        "                'positive': {\n",
        "                    'input_ids': positive['input_ids'],\n",
        "                    'attention_mask': positive['attention_mask']\n",
        "                }\n",
        "            }\n",
        "            yield sample\n",
        "\n",
        "class ArxivValDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, val_df):\n",
        "        self.df = val_df\n",
        "        \n",
        "    def __getitem__(self, idx):\n",
        "        doc = self.df.iloc[idx]\n",
        "        anchor = tokenizer(\n",
        "            doc['summarized_new_title'], max_length=512,\n",
        "            padding='max_length', truncation=True,\n",
        "            return_tensors='pt'\n",
        "        )\n",
        "        positive = tokenizer(\n",
        "            doc['input_text'], max_length=512,\n",
        "            padding='max_length', truncation=True,\n",
        "            return_tensors='pt'\n",
        "        )\n",
        "        sample = {\n",
        "            'anchor': {\n",
        "                'input_ids': anchor['input_ids'],\n",
        "                'attention_mask': anchor['attention_mask']\n",
        "            },\n",
        "            'positive': {\n",
        "                'input_ids': positive['input_ids'],\n",
        "                'attention_mask': positive['attention_mask']\n",
        "            }\n",
        "        }\n",
        "        return sample\n",
        "    \n",
        "    def __len__(self):\n",
        "        return len(self.df)\n",
        "\n",
        "arxiv_train = ArxivDataset(train_metadata)\n",
        "arxiv_val = ArxivValDataset(val_metadata)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0f6255c6-698a-40ff-8bfc-ce6a04257306",
      "metadata": {
        "id": "0f6255c6-698a-40ff-8bfc-ce6a04257306"
      },
      "source": [
        "Define dataset and dataloader..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "57e3ef09-7916-42e4-bdaf-42d491235424",
      "metadata": {
        "id": "57e3ef09-7916-42e4-bdaf-42d491235424"
      },
      "outputs": [],
      "source": [
        "batch_size = 16  # bigger always better for MNR\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    arxiv_train, batch_size=batch_size\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c7c0924e-69d9-435c-a35a-3836ab930ffa",
      "metadata": {
        "id": "c7c0924e-69d9-435c-a35a-3836ab930ffa"
      },
      "outputs": [],
      "source": [
        "val_loader = torch.utils.data.DataLoader(\n",
        "    arxiv_val, batch_size=batch_size\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7e079a12-5213-46bc-b1b8-f18b6672b517",
      "metadata": {
        "id": "7e079a12-5213-46bc-b1b8-f18b6672b517"
      },
      "source": [
        "Initialize a model..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "97b0609f-3284-4ff3-bf9b-1abb7ceba549",
      "metadata": {
        "id": "97b0609f-3284-4ff3-bf9b-1abb7ceba549",
        "outputId": "60dd90ec-83f4-459a-c5e5-394e1ca6ed11",
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cuda:1\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "BertModel(\n",
              "  (embeddings): BertEmbeddings(\n",
              "    (word_embeddings): Embedding(30522, 384, padding_idx=0)\n",
              "    (position_embeddings): Embedding(512, 384)\n",
              "    (token_type_embeddings): Embedding(2, 384)\n",
              "    (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (encoder): BertEncoder(\n",
              "    (layer): ModuleList(\n",
              "      (0): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (key): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (value): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=384, out_features=1536, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=1536, out_features=384, bias=True)\n",
              "          (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (1): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (key): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (value): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=384, out_features=1536, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=1536, out_features=384, bias=True)\n",
              "          (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (2): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (key): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (value): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=384, out_features=1536, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=1536, out_features=384, bias=True)\n",
              "          (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (3): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (key): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (value): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=384, out_features=1536, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=1536, out_features=384, bias=True)\n",
              "          (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (4): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (key): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (value): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=384, out_features=1536, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=1536, out_features=384, bias=True)\n",
              "          (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (5): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (key): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (value): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=384, out_features=1536, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=1536, out_features=384, bias=True)\n",
              "          (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (6): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (key): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (value): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=384, out_features=1536, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=1536, out_features=384, bias=True)\n",
              "          (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (7): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (key): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (value): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=384, out_features=1536, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=1536, out_features=384, bias=True)\n",
              "          (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (8): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (key): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (value): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=384, out_features=1536, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=1536, out_features=384, bias=True)\n",
              "          (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (9): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (key): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (value): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=384, out_features=1536, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=1536, out_features=384, bias=True)\n",
              "          (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (10): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (key): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (value): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=384, out_features=1536, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=1536, out_features=384, bias=True)\n",
              "          (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (11): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (key): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (value): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=384, out_features=384, bias=True)\n",
              "            (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=384, out_features=1536, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=1536, out_features=384, bias=True)\n",
              "          (LayerNorm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (pooler): BertPooler(\n",
              "    (dense): Linear(in_features=384, out_features=384, bias=True)\n",
              "    (activation): Tanh()\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoModel\n",
        "\n",
        "# set hardware device\n",
        "device = 'cuda:1' if torch.cuda.is_available() else 'cpu'\n",
        "print(device)\n",
        "\n",
        "model = AutoModel.from_pretrained(model_id).to(device)\n",
        "\n",
        "model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9ed323ff-cace-4c56-b35a-c1bb9db362cc",
      "metadata": {
        "id": "9ed323ff-cace-4c56-b35a-c1bb9db362cc"
      },
      "source": [
        "Defining layers/steps required for MNR loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3d1d4b07-84dd-417c-8774-732947f62b42",
      "metadata": {
        "id": "3d1d4b07-84dd-417c-8774-732947f62b42"
      },
      "outputs": [],
      "source": [
        "def mean_pool(token_embeds, attention_mask):\n",
        "    in_mask = attention_mask.unsqueeze(-1).expand(\n",
        "        token_embeds.size()\n",
        "    ).float()\n",
        "    pool = torch.sum(token_embeds*in_mask, 1) / torch.clamp(\n",
        "        in_mask.sum(1), min=1e-9\n",
        "    )\n",
        "    return pool"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "986dd54d-ed9c-4c1d-bb4b-ada93cee276a",
      "metadata": {
        "id": "986dd54d-ed9c-4c1d-bb4b-ada93cee276a",
        "outputId": "acf9a972-f5b3-4296-9349-e0ce367ffe07"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "CrossEntropyLoss()"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cos_sim = torch.nn.CosineSimilarity()\n",
        "loss_func = torch.nn.CrossEntropyLoss()\n",
        "# move to device\n",
        "loss_func.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f624f034-1c79-42dc-9470-1e9491848db4",
      "metadata": {
        "id": "f624f034-1c79-42dc-9470-1e9491848db4"
      },
      "source": [
        "Define optimizer..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6493fe74-bad8-4652-8e91-8bbb7c1615be",
      "metadata": {
        "id": "6493fe74-bad8-4652-8e91-8bbb7c1615be",
        "outputId": "d14d0e49-e9fb-48ec-bdb5-12d2c06e8046"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2022-11-09 07:59:33.482032: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n"
          ]
        }
      ],
      "source": [
        "from transformers.optimization import get_linear_schedule_with_warmup\n",
        "\n",
        "# init Adam optimizer\n",
        "optim = torch.optim.Adam(model.parameters(), lr=2e-5)\n",
        "\n",
        "train_steps = 100_000\n",
        "\n",
        "# setup warmup for ~10% of steps\n",
        "total_steps = int(train_steps / batch_size)\n",
        "warmup_steps = int(.1 * total_steps)\n",
        "scheduler = get_linear_schedule_with_warmup(\n",
        "    optim, num_warmup_steps=warmup_steps,\n",
        "    num_training_steps=total_steps-warmup_steps\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c23a29ba-10c7-487d-91e1-367e7ae22d6a",
      "metadata": {
        "id": "c23a29ba-10c7-487d-91e1-367e7ae22d6a"
      },
      "source": [
        "Define the \"labels\" which are just a ranked list of values and will not change"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0d730751-4a1e-4c48-9d3b-e16866a52f16",
      "metadata": {
        "id": "0d730751-4a1e-4c48-9d3b-e16866a52f16",
        "outputId": "35d4ff7c-7d93-40bd-c49d-566b4b4ea0ea"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15],\n",
              "       device='cuda:1')"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "labels = torch.tensor(\n",
        "    range(batch_size), dtype=torch.long, device=device\n",
        ")\n",
        "labels"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1a93db37-edeb-4f9c-a7bf-d68d5831d65b",
      "metadata": {
        "id": "1a93db37-edeb-4f9c-a7bf-d68d5831d65b"
      },
      "source": [
        "Prep W&B logging"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b889e5e8-bb4d-414f-bbcd-e57dd68c2942",
      "metadata": {
        "id": "b889e5e8-bb4d-414f-bbcd-e57dd68c2942",
        "outputId": "fac6cb5b-2060-4c0b-9ef2-863bca01d46d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "wandb.watch(model, log_freq=100)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "be0377f5-4951-4af5-b600-560d56a6b74d",
      "metadata": {
        "id": "be0377f5-4951-4af5-b600-560d56a6b74d"
      },
      "source": [
        "Define a train function for single batch."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d855f19e-efbd-42d3-a3d3-c7e3db7e497b",
      "metadata": {
        "id": "d855f19e-efbd-42d3-a3d3-c7e3db7e497b"
      },
      "outputs": [],
      "source": [
        "def prep_batch(batch, device):\n",
        "    # prepare batches and move inputs to device\n",
        "    anchor_ids = batch['anchor']['input_ids'].squeeze(1).to(device)\n",
        "    anchor_mask = batch['anchor']['attention_mask'].squeeze(1).to(device)\n",
        "    pos_ids = batch['positive']['input_ids'].squeeze(1).to(device)\n",
        "    pos_mask = batch['positive']['attention_mask'].squeeze(1).to(device)\n",
        "    return anchor_ids, anchor_mask, pos_ids, pos_mask\n",
        "\n",
        "def step(model, batch, device):\n",
        "    anchor_ids, anchor_mask, pos_ids, pos_mask = prep_batch(\n",
        "        batch, device\n",
        "    )\n",
        "    # extract token embeddings from MiniLM\n",
        "    anchor_embeds = model(\n",
        "        anchor_ids, attention_mask=anchor_mask\n",
        "    )[0]  # all token-level embeddings\n",
        "    pos_embeds = model(\n",
        "        pos_ids, attention_mask=pos_mask\n",
        "    )[0]  # same again but for positives\n",
        "    # get mean pooled vectors\n",
        "    anchor_embeds = mean_pool(anchor_embeds, anchor_mask)\n",
        "    pos_embeds = mean_pool(pos_embeds, pos_mask)\n",
        "    # calculate cosine similarities\n",
        "    scores = torch.stack([\n",
        "        cos_sim(\n",
        "            anchor.reshape(1, anchor.shape[0]),\n",
        "            pos_embeds\n",
        "        ) for anchor in anchor_embeds\n",
        "    ])\n",
        "    # check labels length (important for final batch)\n",
        "    if anchor_ids.shape[0] != batch_size:\n",
        "        modified_labels = labels[:anchor_ids.shape[0]]\n",
        "        # calculate the loss\n",
        "        loss = loss_func(scores*scale, modified_labels)\n",
        "    else:\n",
        "        # calculate the loss (without modified labels tensor)\n",
        "        loss = loss_func(scores*scale, labels)\n",
        "    return model, loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7c3e204f-1afc-4e6e-8e46-ee0515771785",
      "metadata": {
        "collapsed": true,
        "id": "7c3e204f-1afc-4e6e-8e46-ee0515771785",
        "jupyter": {
          "outputs_hidden": true
        },
        "outputId": "0ca870c8-7fb1-4e0b-8398-a399c4c07682",
        "tags": []
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
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            "Epoch: 0: : 10625it [1:16:09,  2.82it/s, loss=0.0135] \n",
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            "Epoch: 0: : 13125it [1:31:56,  2.80it/s, loss=0.124]  \n",
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            "Epoch: 0: : 14375it [1:39:41,  2.81it/s, loss=0.305]  \n",
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            "Epoch: 0: : 18125it [2:02:57,  2.78it/s, loss=0.0178] \n",
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            "Epoch: 0: : 28125it [3:05:02,  2.81it/s, loss=0.0273] \n",
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            "Validation: 100%|██████████| 70/70 [00:08<00:00,  7.94it/s, loss=0.00549]\u001b[A\n",
            "Epoch: 0: : 73750it [7:48:49,  2.80it/s, loss=0.123]  \n",
            "  0%|          | 0/70 [00:00<?, ?it/s]\u001b[A\n",
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            "Validation:   7%|▋         | 5/70 [00:00<00:08,  7.84it/s, loss=0.0251]IOPub message rate exceeded.\n",
            "The Jupyter server will temporarily stop sending output\n",
            "to the client in order to avoid crashing it.\n",
            "To change this limit, set the config variable\n",
            "`--ServerApp.iopub_msg_rate_limit`.\n",
            "\n",
            "Current values:\n",
            "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
            "ServerApp.rate_limit_window=3.0 (secs)\n",
            "\n",
            "Epoch: 0: : 76250it [8:04:23,  2.80it/s, loss=0.203]  \n",
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            "Epoch: 0: : 95000it [10:01:19,  2.51it/s, loss=0.23]   \n",
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            "Validation:  16%|█▌        | 11/70 [00:01<00:07,  7.94it/s, loss=0.0363]\u001b[AIOPub message rate exceeded.\n",
            "The Jupyter server will temporarily stop sending output\n",
            "to the client in order to avoid crashing it.\n",
            "To change this limit, set the config variable\n",
            "`--ServerApp.iopub_msg_rate_limit`.\n",
            "\n",
            "Current values:\n",
            "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
            "ServerApp.rate_limit_window=3.0 (secs)\n",
            "\n",
            "Epoch: 0: : 96250it [10:09:07,  2.78it/s, loss=0.171]  \n",
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            "Epoch: 0: : 96875it [10:13:01,  2.79it/s, loss=0.0715] \n",
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            "Epoch: 0: : 96962it [10:13:41,  2.78it/s, loss=0.0673] wandb: Network error (ReadTimeout), entering retry loop.\n",
            "Epoch: 0: : 97268it [10:15:31,  2.79it/s, loss=0.241]  wandb: Network error (ReadTimeout), entering retry loop.\n",
            "Epoch: 0: : 97500it [10:16:55,  2.50it/s, loss=0.331]  \n",
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            "Epoch: 0: : 97620it [10:17:47,  2.80it/s, loss=0.149]  wandb: Network error (ReadTimeout), entering retry loop.\n",
            "Epoch: 0: : 98125it [10:20:49,  2.79it/s, loss=0.00569]\n",
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            "Validation:  84%|████████▍ | 59/70 [00:07<00:01,  7.80it/s, loss=0.142]\u001b[A\n",
            "Validation:  84%|████████▍ | 59/70 [00:07<00:01,  7.80it/s, loss=0.142]\u001b[A\n",
            "Validation:  84%|████████▍ | 59/70 [00:07<00:01,  7.80it/s, loss=0.0357]\u001b[A\n",
            "Validation:  86%|████████▌ | 60/70 [00:07<00:01,  7.83it/s, loss=0.0357]\u001b[A\n",
            "Validation:  86%|████████▌ | 60/70 [00:07<00:01,  7.83it/s, loss=0.0357]\u001b[A\n",
            "Validation:  86%|████████▌ | 60/70 [00:07<00:01,  7.83it/s, loss=0.0662]\u001b[A\n",
            "Validation:  87%|████████▋ | 61/70 [00:07<00:01,  7.81it/s, loss=0.0662]\u001b[A\n",
            "Validation:  87%|████████▋ | 61/70 [00:07<00:01,  7.81it/s, loss=0.0662]\u001b[A\n",
            "Validation:  87%|████████▋ | 61/70 [00:07<00:01,  7.81it/s, loss=0.0499]\u001b[A\n",
            "Validation:  89%|████████▊ | 62/70 [00:07<00:01,  7.81it/s, loss=0.0499]\u001b[A\n",
            "Validation:  89%|████████▊ | 62/70 [00:07<00:01,  7.81it/s, loss=0.0499]\u001b[A\n",
            "Validation:  89%|████████▊ | 62/70 [00:08<00:01,  7.81it/s, loss=0.0923]\u001b[A\n",
            "Validation:  90%|█████████ | 63/70 [00:08<00:00,  7.84it/s, loss=0.0923]\u001b[A\n",
            "Validation:  90%|█████████ | 63/70 [00:08<00:00,  7.84it/s, loss=0.0923]\u001b[A\n",
            "Validation:  90%|█████████ | 63/70 [00:08<00:00,  7.84it/s, loss=0.05]  \u001b[A\n",
            "Validation:  91%|█████████▏| 64/70 [00:08<00:00,  7.83it/s, loss=0.05]\u001b[A\n",
            "Validation:  91%|█████████▏| 64/70 [00:08<00:00,  7.83it/s, loss=0.05]\u001b[A\n",
            "Validation:  91%|█████████▏| 64/70 [00:08<00:00,  7.83it/s, loss=0.332]\u001b[A\n",
            "Validation:  93%|█████████▎| 65/70 [00:08<00:00,  7.84it/s, loss=0.332]\u001b[A\n",
            "Validation:  93%|█████████▎| 65/70 [00:08<00:00,  7.84it/s, loss=0.332]\u001b[A\n",
            "Validation:  93%|█████████▎| 65/70 [00:08<00:00,  7.84it/s, loss=0.0457]\u001b[A\n",
            "Validation:  94%|█████████▍| 66/70 [00:08<00:00,  7.83it/s, loss=0.0457]\u001b[A\n",
            "Validation:  94%|█████████▍| 66/70 [00:08<00:00,  7.83it/s, loss=0.0457]\u001b[A\n",
            "Validation:  94%|█████████▍| 66/70 [00:08<00:00,  7.83it/s, loss=0.0249]\u001b[A\n",
            "Validation:  96%|█████████▌| 67/70 [00:08<00:00,  7.86it/s, loss=0.0249]\u001b[A\n",
            "Validation:  96%|█████████▌| 67/70 [00:08<00:00,  7.86it/s, loss=0.0249]\u001b[A\n",
            "Validation:  96%|█████████▌| 67/70 [00:08<00:00,  7.86it/s, loss=0.0798]\u001b[A\n",
            "Validation:  97%|█████████▋| 68/70 [00:08<00:00,  7.88it/s, loss=0.0798]\u001b[A\n",
            "Validation:  97%|█████████▋| 68/70 [00:08<00:00,  7.88it/s, loss=0.0798]\u001b[A\n",
            "Validation:  97%|█████████▋| 68/70 [00:08<00:00,  7.88it/s, loss=0.0528]\u001b[A\n",
            "Validation:  99%|█████████▊| 69/70 [00:08<00:00,  7.91it/s, loss=0.0528]\u001b[A\n",
            "Validation:  99%|█████████▊| 69/70 [00:08<00:00,  7.91it/s, loss=0.0528]\u001b[A\n",
            "Validation: 100%|██████████| 70/70 [00:08<00:00,  7.90it/s, loss=0.00549]\u001b[A\n",
            "Epoch: 0: : 103730it [10:55:49,  2.79it/s, loss=0.0676] "
          ]
        }
      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "scale = 20.0  # use to increase loss values\n",
        "epochs = 1  # 1 is typically enough for MNR training\n",
        "val_check = int(10_000/batch_size)  # how often calculate val_loss\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    model.train()\n",
        "    loop = tqdm(train_loader, leave=True, total=total_steps)\n",
        "    # iterate through all batches in current epoch\n",
        "    for i, batch in enumerate(loop):\n",
        "        optim.zero_grad()\n",
        "        # this is training\n",
        "        model, loss = step(model, batch, device)\n",
        "        # using loss, calculate gradients and optimize\n",
        "        loss.backward()\n",
        "        optim.step()\n",
        "        # update learning rate scheduler\n",
        "        scheduler.step()\n",
        "        # update progress bar\n",
        "        loop.set_description(f\"Epoch: {epoch}\")\n",
        "        loop.set_postfix(loss=loss.item())\n",
        "        # see if to calculate validation loss\n",
        "        if i % val_check == 0:\n",
        "            val_loss_avg = []\n",
        "            model.eval()\n",
        "            val_loop = tqdm(val_loader, leave=True)\n",
        "            for j, val_batch in enumerate(val_loop):\n",
        "                model, val_loss = step(model, val_batch, device)\n",
        "                val_loop.set_description(f\"Validation\")\n",
        "                val_loop.set_postfix(loss=val_loss.item())\n",
        "                val_loss_avg.append(val_loss.item())\n",
        "            model.train()\n",
        "            \n",
        "        if i % 10 == 0:\n",
        "            wandb.log({'val_loss': sum(val_loss_avg)/len(val_loss_avg), 'loss': loss.item()})"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "44055232-2484-4d11-990b-4b91839bb331",
      "metadata": {
        "id": "44055232-2484-4d11-990b-4b91839bb331"
      },
      "source": [
        "## Saving the Model with W&B\n",
        "\n",
        "We can store the model as an artifact, first we use `save_pretrained` to save everything locally:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "15455ba3-e4be-434d-84ba-298aaac09cba",
      "metadata": {
        "id": "15455ba3-e4be-434d-84ba-298aaac09cba",
        "outputId": "06d924e0-7a21-4074-f502-cce092dac920",
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('minilm-arxiv/tokenizer_config.json',\n",
              " 'minilm-arxiv/special_tokens_map.json',\n",
              " 'minilm-arxiv/vocab.txt',\n",
              " 'minilm-arxiv/added_tokens.json',\n",
              " 'minilm-arxiv/tokenizer.json')"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.save_pretrained('minilm-arxiv')\n",
        "tokenizer.save_pretrained('minilm-arxiv')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "39799422-4ba4-4640-91d3-8ecffa9b5831",
      "metadata": {
        "id": "39799422-4ba4-4640-91d3-8ecffa9b5831"
      },
      "source": [
        "Then we create an artifact that stores all of the above..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "22ea940c-3cea-491a-a2b4-c4e2c208a72f",
      "metadata": {
        "id": "22ea940c-3cea-491a-a2b4-c4e2c208a72f",
        "outputId": "3d9683cc-876d-4c72-853f-88f8ac8fc0bc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['minilm-arxiv/vocab.txt',\n",
              " 'minilm-arxiv/tokenizer.json',\n",
              " 'minilm-arxiv/pytorch_model.bin',\n",
              " 'minilm-arxiv/special_tokens_map.json',\n",
              " 'minilm-arxiv/tokenizer_config.json',\n",
              " 'minilm-arxiv/config.json']"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from pathlib import Path\n",
        "\n",
        "model_artifact = wandb.Artifact(\n",
        "    name=\"minilm-arxiv\",\n",
        "    type=\"model\"\n",
        ")\n",
        "\n",
        "paths = [str(x) for x in Path('./minilm-arxiv').glob('*')]\n",
        "paths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "24b6b8bb-5bf9-4fdf-b071-ee0cbac64235",
      "metadata": {
        "id": "24b6b8bb-5bf9-4fdf-b071-ee0cbac64235",
        "outputId": "513edb48-d5e1-4248-86ae-11d7b0daa3ea"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<wandb.sdk.wandb_artifacts.Artifact at 0x7f7ffc4ae4d0>"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "for path in paths:\n",
        "    model_artifact.add_file(path)\n",
        "wandb.run.log_artifact(model_artifact)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "xDbp2WEpZsfi",
      "metadata": {
        "id": "xDbp2WEpZsfi"
      },
      "source": [
        "We can find the artifact over in the W&B dashboard:"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "T2mvWDHFZOTl",
      "metadata": {
        "id": "T2mvWDHFZOTl"
      },
      "source": [
        "![image_2022-11-10_103601546.png]()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e1ed8678-990d-4923-b1ee-74bba116632e",
      "metadata": {
        "id": "e1ed8678-990d-4923-b1ee-74bba116632e"
      },
      "source": [
        "With that we're ready to move on to the next notebook [`02-encode.ipynb`](https://colab.research.google.com/github/pinecone-io/examples/blob/master/learn/projects/training-with-wandb/02-encode.ipynb)."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": []
    },
    "environment": {
      "kernel": "python3",
      "name": "common-cu110.m95",
      "type": "gcloud",
      "uri": "gcr.io/deeplearning-platform-release/base-cu110:m95"
    },
    "kernelspec": {
      "display_name": "Python 3.10.7 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.7 (main, Sep 14 2022, 22:38:23) [Clang 14.0.0 (clang-1400.0.29.102)]"
    },
    "vscode": {
      "interpreter": {
        "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}
